Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Beyond Uniform Lipschitz Condition in Differentially Private Optimization
Authors: Rudrajit Das, Satyen Kale, Zheng Xu, Tong Zhang, Sujay Sanghavi
ICML 2023 | Venue PDF | LLM Run Details | Input Tokens: 42,210 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,995 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We verify the efficacy of our recommendation via experiments on 8 datasets. In Section 5.2, we corroborate our recommendation with experiments on four1 vision datasets, viz., Caltech-256 (Griffin et al., 2007), Food-101 (Bossard et al., 2014), CIFAR-100 and CIFAR-10, and two language datasets, viz., Tweet Eval Emoji (Barbieri et al., 2018) and Emotion (Saravia et al., 2018). |
| Researcher Affiliation | Collaboration | Rudrajit Das * 1 Satyen Kale 2 Zheng Xu 2 Tong Zhang 2 3 Sujay Sanghavi 1 1UT Austin 2Google Research 3HKUST. |
| Pseudocode | Yes | Algorithm 1 DP-SGD (Abadi et al., 2016) |
| Open Source Code | No | The paper mentions using a third-party library ('Py Torch s Opacus library') but does not state that the authors' own implementation code for the described methodology is open-source or provide a link to it. |
| Open Datasets | Yes | Our experiments here are conducted on four vision datasets available in Torchvision, viz., Caltech-256 (Griffin et al., 2007), Food-101 (Bossard et al., 2014), CIFAR-100 and CIFAR-10, and two language datasets, viz., Tweet Eval Emoji (Barbieri et al., 2018) and Emotion (Saravia et al., 2018). |
| Dataset Splits | No | The paper refers to 'test accuracy' and hyperparameter tuning but does not specify the exact train/validation/test split percentages, sample counts, or methodology used for data partitioning. |
| Hardware Specification | Yes | A single NVIDIA TITAN Xp GPU was used for all the experiments in this paper. |
| Software Dependencies | No | Py Torch s Opacus library (Yousefpour et al., 2021) is used for private training. (No version numbers provided for PyTorch or Opacus). |
| Experiment Setup | Yes | We consider three privacy levels (2, 10 5)-DP, (4, 10 5)-DP and (6, 10 5)-DP, with batch size = 500. We test several values of the clip norm τ, viz., the 0th, 10th, 20th, 40th, 80th and 100th percentile of the per-sample Lipschitz constants... For each value of τ, we tune over several values of the constant learning rate η, viz., {0.0001, 0.0003, 0.0006, 0.001, 0.003, 0.006, 0.01, 0.03, 0.06, 0.1, 0.3, 0.6, 1, 3, 6, 10}. |